Which readiness profiles do the data show?

Evidence

Analysis

How the profiles were formed

The thesis applies K-means to the six z-standardised dimension scores, with ten random starts and a fixed seed. Three criteria determined the number of clusters: the elbow curve shows only small improvements after k = 3, the Krzanowski–Lai index peaks at k = 3, the silhouette at k = 2. The thesis chooses three profiles because they offer more resolution than a plain high–low split.[Probst 2026, Figure 6]

The original labels are Leaders, Followers and Laggards. In this edition we call them the advanced profile, the intermediate profile and the early stage. The labels describe starting points, not a ranking of organisations: an organisation in the early stage has different priorities, not less worth.

Where the profiles separate

All six dimensions differ significantly between the profiles, which is by construction with K-means. What is telling is how strongly: on Dynamic Capabilities the gap between the advanced profile and the early stage is 2.29 scale points, on External Alignment 1.15. Two pairwise comparisons are not significant: intermediate versus early stage on External Alignment (p = .996) and on Data Engine (p = .069).[Probst 2026, Appendix C]

The advanced profile has its lowest score on External Alignment (3.58), as does the intermediate profile (2.44). The early stage sits lowest on Dynamic Capabilities (1.84). Within the profiles, scores are homogeneous on five dimensions (SD 0.39 to 0.67); only External Alignment spreads widely across all three.[Probst 2026, Table 5]

External check and composition

The composition of the profiles is exploratory: many cells hold fewer than five cases, which is why the thesis deliberately runs no significance tests. It still names three patterns: the concentration of technology, media and telecommunications in the advanced profile, the overrepresentation of large organisations in the early stage, and the role of the respondent.[Probst 2026, Appendix E]

Teklens interpretation

For your product team

The study describes organisations across sectors. Applying it to software product teams is our interpretation, not an empirical finding about product teams.

Different starting points call for different priorities. Our reading per profile, without assigning an organisation to a profile from six answers.

• Early stage: one workflow, not six dimensions

  Whoever sits below 3 everywhere should not lift all dimensions at once. One workflow with a clear goal, named sources and one approval achieves more than a programme.

• Intermediate: data and adaptation

  The intermediate profile has backing, people and technology, but no better data basis than the early stage and little routine in reconfiguring. Here it pays to maintain sources and to change the workflow after every initiative.

• Advanced: close the external gap

  Advanced organisations have few internal gaps, but governance, ecosystem and regulation stay below 4. Making accountability and operating constraints visible is the next step here.

Scope & limits

• 64 organisations allow an exploratory profiling, not robust subgroup comparisons. The thesis names the sample size as its most important limitation.
• Two and three clusters were both statistically viable; the three-cluster solution is a reasoned decision, not an unambiguous statistical outcome.
• The profiles describe this sample. They are not market shares and no basis for assigning a single organisation from a few answers.

Questions & answers

Sources

1. Probst 2026, Table 5 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Table 5 and Figure 7 – Dimension scores by profile (Section 4.4.2). Sample: n = 64; profiles of n = 12, 27 and 25 · Scale: Maturity scale 1–5 · Limits: Profiles from K-means on the same six dimensions; differences between profiles are large by construction.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
2. Probst 2026, Figure 6 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Figure 6 – Elbow, silhouette and KL index for the number of clusters (Section 4.4.1). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Cluster indices · Limits: Two and three clusters were both statistically viable; three were chosen for interpretability.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
3. Probst 2026, Figure 8 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Figure 8 – Distribution of Dynamic Capabilities scores by profile (Section 4.4.2). Sample: n = 64 individual scores; profiles of n = 12, 27 and 25 · Scale: Maturity scale 1–5 · Limits: Self-assessment, exploratory, not representative.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
4. Probst 2026, Appendix C · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix C – One-way ANOVA and Tukey HSD per dimension. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: F values, df (2, 61), p · Limits: Describes how sharply the clusters separate on the clustering variables themselves; not a hypothesis test.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
5. Probst 2026, Appendix D · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix D – External validation of the profiles via depth of AI adoption (Q23). Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Composite 4–20 across four business functions · Limits: Q23 was not part of the clustering; association, not causation.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
6. Probst 2026, Appendix E · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Appendix E – Profile composition by industry, firm size and hierarchical position. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Frequencies · Limits: Many cells hold fewer than five cases; the thesis deliberately runs no significance tests and reads the patterns as exploratory tendencies.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.
7. Probst 2026, Section 4.4 · Capturing AI's Potential: How Ready Are Organisations in the DACH Region? (2026). Section 4.4 – Cluster analysis, criterion validity and composition. Sample: n = 64 cleaned responses, Germany and Switzerland · Scale: Maturity scale 1–5 · Limits: Self-assessment, exploratory, not representative.Probst, F. (2026). Capturing AI's Potential: How Ready Are Organisations in the DACH Region? An Empirical Analysis Using an Integrated TOE–DCT Framework. Master's thesis, University of St.Gallen, Institute of Information Systems and Digital Business. Supervisor: Prof. Dr. Ingrid Bauer-Hänsel; co-supervisor: Prof. Dr. Benjamin van Giffen. St. Gallen, 21 August 2026. Conducted in cooperation with Teklens.

Methodology & sources